Table-to-text generation, a long-standing challenge in natural language
generation, has remained unexplored through the lens of subjectivity.
Subjectivity here encompasses the comprehension of information derived from the
table that cannot be described solely by objective data. Given the absence of
pre-existing datasets, we introduce the Ta2TS dataset with 3849 data instances.
We perform the task of fine-tuning sequence-to-sequence models on the
linearized tables and prompting on popular large language models. We analyze
the results from a quantitative and qualitative perspective to ensure the
capture of subjectivity and factual consistency. The analysis shows the
fine-tuned LMs can perform close to the prompted LLMs. Both the models can
capture the tabular data, generating texts with 85.15% BERTScore and 26.28%
Meteor score. To the best of our knowledge, we provide the first-of-its-kind
dataset on tables with multiple genres and subjectivity included and present
the first comprehensive analysis and comparison of different LLM performances
on this task.

通过对线性化表格进行序列到序列模型的微调和在流行的大型语言模型上进行提示，我们分析了从定量和定性的角度来确保捕捉主观性和事实的一致性的结果，显示出微调的 LMs 可以接近提示的 LLMs。根据我们的了解，我们提供了一种多类型和包含主观性的表格数据集，并对不同的 LLM 性能进行了首次全面的分析和比较。